Michael
a0042d4aba
Previous year inference picked ``period_end_iso[:4]`` for every
short date, which fails on statements that cross the Dec/Jan
boundary. A "12/30" row in a 2024-12-16 to 2025-01-15 statement
got 2025-12-30 (wrong) instead of 2024-12-30.
New cascade for ``_infer_year_for_short_date``:
1. **``override_year``** — caller supplies it (new ``"Override
year for short dates"`` field in Scan options). Beats every
heuristic. Empty by default; the page validates the value
is a 4-digit-looking integer in 1900-2100 and falls back to
automatic on garbage input.
2. **Statement period start + end** — the function now takes
BOTH dates and generates candidates with every distinct year
in the period (one year for same-year statements, two for
Dec/Jan boundaries). The picker scores each candidate by
distance from the period: candidates inside the period
score 0, candidates outside score ``min(|days from start|,
|days from end|)``. Lowest-distance candidate wins. So:
- ``12/30`` + period 2024-12-16 to 2025-01-15 → 2024-12-30
(inside period, score 0)
- ``01/05`` + same period → 2025-01-05 (inside, score 0)
- ``12/15`` + same period → 2024-12-15 (1 day before,
closer than 2025-12-15 which is 11 months after)
3. **``filename_year_hint``** — fallback when the statement
period regex misses the bank's specific layout. The page
passes ``year_from_filename(upload.name)`` automatically so
files like ``eStmt_2025-01-13.pdf`` get year 2025 even if
the PDF's text doesn't yield a parseable period. The regex
matches the first ``20XX`` token bounded by non-digits.
Both new helpers (``year_from_filename`` and the new
``_try_short_date_with_year`` factor-out) are exported and
tested. 16 new tests cover: within-period inference (same-year
sanity), Dec/Jan boundary cases for both sides, the
just-before-period closer-distance case, override priority,
filename fallback, no-signal None, dash-format / month-name
shorthand round-trip, garbage input, filename year extraction
(eStmt pattern, embedded, first-match-wins, no-match, empty).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
🌐 Language: English · Español
DataTools
Local CSV / Excel cleaning. CLI + browser GUI, no cloud, no install ceremony. GUI ships with English and Spanish language packs.
Tools
| # | Tool | Status |
|---|---|---|
| 01 | Find Duplicates — exact + fuzzy match, 5 normalizers, survivor rules, audit | Ready |
| 02 | Clean Text — whitespace, smart chars, BOM, line endings, case ops | Ready |
| 03 | Standardize Formats — dates, phones, emails, addresses, names, currencies, booleans | Ready |
| 04 | Fix Missing Values — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies | Ready |
| 05 | Map Columns — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder | Ready |
| 06 | Find Unusual Values | Coming Soon |
| 07 | Combine Files | Coming Soon |
| 08 | Quality Check | Coming Soon |
| 09 | Automated Workflows — chain tools with recommended (not forced) order, save/load JSON, automate weekly cleanups | Ready |
Download (non-technical users)
Pre-built installers — no Python required:
| Platform | Download | First-launch note |
|---|---|---|
| macOS | DataTools-X.Y.Z-mac.dmg |
Drag DataTools.app into /Applications, then double-click. |
| Windows | DataTools-X.Y.Z-win-setup.exe |
Run the installer; launches from Start Menu. |
| Linux | DataTools-X.Y.Z-linux-x86_64.AppImage |
chmod +x the file, then double-click. |
Latest release: see GitHub Releases (or the Gumroad listing). The installers are ~150–200 MB; the launcher boots a local server at http://127.0.0.1:8501 and opens your browser. Nothing is sent to the cloud.
Install from source (developers)
pip install -r requirements.txt
Python 3.10+ required.
Run
GUI (recommended):
streamlit run src/gui/app.py
CLI — seven entry points:
python -m src.cli customers.csv [--apply] # dedup
python -m src.cli_text_clean messy.csv [--apply] # text clean
python -m src.cli_format intl.csv [--apply] # format standardize (auto-streams >100 MB)
python -m src.cli_missing holes.csv [--apply] # missing values
python -m src.cli_column_map vendor.csv [--apply] # column mapper
python -m src.cli_pipeline any_file.csv [--apply] # chain tools end-to-end
python -m src.cli_analyze any_file.csv [--json] # scan only
Every CLI runs preview-only by default; add --apply to write output.
Language
The GUI sidebar has a language picker. Packs ship for English and Español (src/i18n/packs/); the choice persists for the session. Adding a language: drop a <code>.json next to en.json mirroring its key tree, then list it in LANGUAGES. See Developer Guide §i18n.
Review & Normalize gate
Every uploaded file passes through a CSV-normalization gate before any tool sees it. The analyzer flags ~15 issue types (whitespace, NBSP / zero-width chars, BOM, encoding, smart punct, dirty headers, null sentinels, mojibake, …) tagged by confidence (high / medium / low) and fix action. The GUI shows each finding with Auto-fix / Skip / Customize, a live before/after preview, and an encoding-override picker. Tool pages refuse to load until the gate passes.
Output
Every run writes:
{input}_<tool>.csv— the cleaned data{input}_changes.csv(text cleaner) or{input}_match_groups.csv(dedup) — audit traillogs/<tool>_YYYYMMDD_HHMMSS.log— debug-level run log
Original input file is never modified.
Docs
- User Guide — install, GUI workflow, gate
- CLI Reference — every flag with recipes
- Requirements — file sizes, encodings, detectors, perf targets
- Technical — architecture, gate internals, fix registry
- Developer Guide — adding fixes / detectors / standardizers
Dependencies
pandas, openpyxl, rapidfuzz, phonenumbers, typer, loguru, charset-normalizer, streamlit. Optional: ftfy for mojibake repair.
License
Proprietary.